- Growth hacking is a marketing term that implies a quick fix for growth, but sustainable growth requires long-term investment.
- A growth team uses a data-driven approach to scale growth after achieving product-market fit.
- Before setting up a growth team, it is important to ensure strong retention.
- Retention metrics vary depending on the nature of the product or service.
- Stitch Fix focused on improving retention by driving the number of second purchases within the first four months.
- The focus on retention helped Stitch Fix achieve organic growth before investing in growth.
- It is important to understand the expected behavior and frequency of product use when setting retention goals.
- Startups should have a single source of truth for data and ensure that user data is saved in one place.

- Startups often save user data in multiple places, so it's important to have one person responsible for the single source of truth data.
- Many startups use external tools like Mixpanel or Amplitude to store and analyze data.
- As a startup grows, they may decide to build their own analytics tools, which requires a dedicated team of data engineers.
- Building internal tools takes time and effort, so starting with external tools is recommended for most companies.
- The CEO's engagement and alignment with using data to make decisions is crucial for the success of a growth team.
- Experimentation is important for making product decisions, as software can track the impacts of changes that humans may not fully comprehend.
- Growth teams should not solely rely on data, as product intuition and gut instincts can also play a role in decision-making.
- Experimentation helps in understanding the impact of changes and making informed decisions for product growth.
- Growth teams should not be solely focused on data, but also consider the vision and values of the company.

- The speaker discusses the importance of balancing strong product design with optimization and experimentation for growth.
- They give examples of Facebook and Instagram, noting that both are optimized and successful but do not feel overly sales-oriented.
- The speaker emphasizes the need to explain the purpose of growth tactics, such as SEO, to avoid negative connotations.
- They also discuss the different growth tactics used by Facebook and Instagram, highlighting the importance of aligning growth strategies with the overall product vision.
- The speaker suggests that founders should set their own heuristics or guidelines for growth, while remaining open to adjustments as the company evolves.
- They explain the process of building a growth team, starting with engineers and product managers and expanding to include other disciplines such as design, data science, and user research.
- The speaker recommends starting the growth team within the product team and emphasizes the importance of the CEO's support and alignment between the head of product and head of growth.
- They discuss the tension between the data-driven nature of growth and the design-focused perspective of product, and the need for effective communication and alignment between the two.

- Communication between the head of product and head of growth is crucial for success
- Facebook is the only company that has successfully maintained a separate growth team
- Clear accountability metrics are important for any structure to work
- Uber had separate product and growth teams, but eventually merged with different subgroups
- The key is to have clear accountability metrics and a North Star metric
- Introducing growth thinking can be uncomfortable for founders as it changes the way things are done at scale

- Growth hacking is a marketing term that suggests quick growth, but sustainable growth requires long-term investment.
- Before setting up a growth team, strong retention must be ensured.
- Stitch Fix focused on improving retention to achieve organic growth before investing in growth.
- Startups should have a single source of truth for data and consider using external tools for data storage and analysis.
- The CEO's engagement and alignment with using data is crucial for the success of a growth team.
- Experimentation and balancing strong product design are important for making informed decisions for product growth.
- Growth teams should consider the vision and values of the company, and align growth strategies with the overall product vision.
- Founders should set their own guidelines for growth, while remaining open to adjustments as the company evolves.
- Building a growth team should start within the product team, with communication and alignment between the head of product and head of growth being crucial for success.
- Clear accountability metrics and a North Star metric are important for any structure to work.
- Introducing growth thinking can be uncomfortable for founders as it changes the way things are done at scale.

Anu Hariharan and Gustaf Alstromer are partners at YC. 

https://twitter.com/anuhariharan

https://twitter.com/gustaf

This episode is a follow-up to Anu's Growth Guide - http://blog.ycombinator.com/growth-guide2017/

The YC podcast is hosted by Craig Cannon - https://twitter.com/craigcannon

All right, Gustav, probably the best place to start here is let's just differentiate growth from growth hacking and all these buzzwords that people talk about.

Sure, so the growth hacking term, to me, is a marketing term.

If you, It's kind of like the new social media manager.

If you want to make people believe you're working on growth, you call yourself a growth hacker.

And if you actually work on growth, you call yourself a product manager working on growth, or an engineer, or a designer.

I think a more kind of deeper explanation might be that you can't hack your way to true sustainable growth you have to invest long-term and The term growth hacking is somehow Give the idea that there's a 1 small thing you can do and just look for that 1 thing and then you're done and that's just not how it works.

So maybe the best entry point into this then is to talk about the growth post and talk about how someone actually might go about investing in growth.

Yeah, you know, before I start on the framework for growth, the reason we actually wrote the post was because a lot of growth state CEOs come to us and ask the number 1 question, which is, What does a growth team do and why should we set up a growth team?

Because let's be honest, it's a relatively new concept.

It didn't exist a decade ago and Facebook was the first to pioneer that.

I think as Gustav mentioned rightfully, growth pros don't like the term hacking because it sort of implies a gut-driven approach.

So that's where a growth team comes into play.

It is a very data and scientific approach to scaling growth.

And right after you achieve that product market fit, which we call the 0 to 1 phase, to accelerate growth, you have to use a data driven approach, and that's when a growth team comes into play.

But before you set up a growth team, the number 1 step you need to check is whether you have strong retention.

Because too often, most companies form a growth team and then wonder why they're not growing fast.

And so the most important step is retention.

And so even if you look at the post, the framing is first check whether you have good retention.

So you want to check whether your users that you acquired in the long term, there is stable retention, which is parallel to the x-axis, and also it's good retention.

So, for example, if you're a social network and you have less than 10% retention and it's stable, it's meaningless.

Because as a social network, you need to have at least 50 to 60%, right?

So it's more important to also benchmark whether your stable retention is good versus benchmarks or better than benchmarks before you start focusing on growth.

So Gustav, when you were at Airbnb, How did you decide on that number?

What retention number were you shooting for?

You like to give the impression that you did everything right from the beginning.

And when I got to Airbnb, we were just kind of like trying to figure things out.

So I don't think that that was the very first thing we looked at.

We were just kind of to try to figure out where are the metrics, what are the things, how does the funnel look like.

If you have great metrics and measuring entire use of funnels through a product, it's pretty easy to figure out your attention rate.

It's pretty easy to figure out where people are dropping off and where the opportunities.

When I joined Airbnb, we were just kind of scrambling to get all the different places we had the metrics together.

And that's kind of where we started just like making sure that we are tracking kind of our user base and our host and our guest in 1 single place.

And there was 1 single source of truth that we can all agree on.

And from that, it gets easier to start measuring some of these things.

And a marketplace, I think today there's a lot of knowledge around how to measure and evaluate the performance of a marketplace.

I had not seen a ton of these things before when I joined Airbnb almost over 5 years ago that I didn't know exactly what to look at.

So we just started looking at all the different metrics and trying to figure out where the opportunities were.

Now today, Airbnb is quite different than most other products in that travel is a very rare occurrence.

You travel once, maybe twice a year, which means that looking at retention on the guest side, you'll have to wait a long time, or you have to have a long kind of, a couple of years of historical metrics that are not like they're actually in good shape.

Very often when you start something you never have a year's worth of metrics.

So that means it's hard to figure that out.

For most companies you just want to try to figure out repeat purchase rate, repeat booking, repeat use of some kind, and that repeat use have to be meaningful.

Like it can't just be like, oh, I sent them a push notification, they came back, and now I have repeat use.

That's not, just the act of coming back isn't meaningful unless you do something that gives you value from the product.

And so Anu, when you were putting data together for the posts, what were those metrics that other companies used to discern like, oh, this is a good use case, rather than just checking the site?

Yeah, I think the most important thing is a sign of the action of usage of your product.

So in the case of Airbnb, I mean, you guys tracked bookings, because that actually shows you booked a room where Airbnb not just visited the site.

In the case of Uber, it was a trip completed, right?

Where you booked a trip and you didn't cancel it, but you actually completed the trip.

In the case of Stitch Fix, this is an interesting 1 because, you know, now obviously there's a lot of talk about how Stitch Fix has done amazingly well.

But for the first 4 years, they actually only focused on retention because they didn't focus on growth because they had organic growth, but they realized that they had a lot of opportunity to improve retention.

So their sort of North Star metric for the longest time was number of second fixes in the first 4 months.

So what that means is how often did the customer order a second stitch fix after the first purchase within the first 4 months because data had shown that people who did that retained much better than others.

So the growth team actually was really focused on driving number of second fixes.

Wow, and so what caused them to wait 4 years rather than just like, you know, just going for it from the beginning.

Because if you have figured out how your product has to evolve to improve retention, then you're much better off scaling growth because now you know how to retain them better.

With a product like Stitch Fix, you know, which is more frequency than travel, because it's not once a year, but at the end of the day, it's apparel, right?

So for the longest time, people still liked walking into a store.

1 of the reasons why Amazon didn't do extremely well for dresses, they do well for standard clothes, right?

And so Stitch Fix had to get that element right.

It was about hitting the right style and the right fit.

And if you just scaled pretty quickly, you would have grown quite fast, but you wouldn't have retained a lot of users and those are wasted marketing dollars, because you have to go back and reacquire them.

So really that metric and that laser focus on retention is really what helped them scale.

And in the process, they were getting a lot of organic growth.

And in the last 1 and a half years, they've actually invested now in growth as well.

But only now, you know, the North Star metric has probably moved to number of first fixes, but for the first 4 years it was about repeat.

So I think they certainly like the entrepreneurs in the investment community have gotten more sophisticated over the last 5, 7 years around how to think about growth and retention.

I think probably 7 years ago people were more obsessed with new user growth and just like people growing top of the funnel and they have learned that it is not everything and it's actually not like even the most important thing.

So I think we've learned that quite a bit.

When I do office hours with YC companies, the first 2 questions I try to figure out is basically what is the kind of ideal or expected behavior, how often do I use this product.

Let's say it's some kind of like, let's say it's like some kind of retail product, like how often am I expecting to do this?

If it's an e-commerce product, how often am I buying this thing?

If it's a subscription, well, it should be on a regular basis.

If it's not a subscription, I still want to figure out how often I do it.

And then once you have that number, and you can get this number by kind of thinking out from what is the ideal use case, or you can look at the data and see how does your most typical active users or retained users actually look like.

And then from there, and this is exactly what we did at Airbnb, we looked at the most typical retained users and what is the typical booking window, like how long does it take between each booking.

And from then you can kind of come up with like, this is the scale.

And then after that you figure out, okay, how many people fit the scale?

How many people actually are able to get to kind of do it the way that the ideal metrics suggest?

And then you kind of figure out your attention rate.

So that's kind of how you start off, and you don't get everything right right away, but that's kind of a good way to start.

Many people don't really know what the expected time between these events should be, but it's very important to figure that early on.

So this is kind of an easy possible question for you guys, but just tools in general, like basic things, like what should someone set up in the beginning to start tracking this stuff?

So when you start, you're probably saving your user data somewhere, so you're probably saving that some kind of hopefully That isn't too complicated, okay, and very often when startups go a little bit bigger It tends to have this data in several different places It's important that you have 1 person responsible for the single source of truth data, the data that everyone can trust so that when you're arguing about metrics, you're not pulling from different sources.

Now, when you make, say, funnels or experiments, often you don't have the data storing it in the same place you store user data.

So you might have that in like a, let's say a mixed panel or maybe you have your own events database, but it's very easy to start with something like mixed panel.

So those are typically the 2 places where you store your data.

There's Mixpanel, there's Amplitude, there's a whole slew of them that YC have funded many of these companies, but there are lots of different ones you can try out.

Segment is 1 way to try them all at the same time.

You can use segment, you can test all of them at the same time.

The external ones are pretty good right now.

When they go to scale Airbnb, many of them end up building something of their own for different reasons.

So at Airbnb, at what point did you decide to roll your own analytics around this stuff?

I think when you have 1 or 2 data engineers you can spare for this project.

So typically 10, 20 engineers, then you have enough that you can say, I'm going to build this myself.

So I think with the case of Airbnb, we started saying, okay, we have our own event database, built on clients to send data to that database from different platforms, iOS, Android, web.

And now then you have to start learning how to make sure that data is always available.

So there's a lot of work that goes into just making sure that data is always available for all the dashboards and all the things you need the data for and make sure that it's correct.

You have to do it, spend a lot of time on this data.

So it's actually quite a lot more work than you think.

But there are some benefits to doing it yourself when you get to that stage.

the growth team when you guys started building your own tool?

So when I started F&B, we were 3 people on the growth team.

That was excluding performance marketing.

There was probably another 3 or 4, 5 people.

We didn't actually build the first database.

It was a separate team, but I think when I joined we were about 35 engineers.

Maybe 3 to 6 months after I joined we decided that this is the time to start investing in internal tools.

And over the next year, we basically built everything that we needed internally.

And it wasn't perfect, so over the next 3 years, we made it perfect.

But within a year after we started, we had all the components that we needed.

And that was basically a segmentation tool that allowed us to show different parts of Airbnb to different users.

It was an event tracking database where we can send all the events.

So we run queries and look at these usage from different clients.

And then we built a kind of experiment framework UI.

So it would automatically do the, show us the different, let's say we run an A-B test, it would show us the different metrics in the control group and the experiment group.

It would automatically calculate statistical significance and power and all these different things that you need.

We didn't have that all down in the beginning.

So in the very common way people start is they store their data somewhere and they have a data scientist kind of in Excel pulling the data from the different groups and then kind of figure out if that something is significant or not.

What are the things you guys did that maybe were too much work, too early, and maybe things you saw Anu putting together the post like, you know what, if you're an early stage company, and even if you're a later stage company, you don't need to do that right now.

This is the effective stuff to focus on in the beginning.

I think 1 of the mistakes that people make early is that the role of using data to inform the rest of the company is kind of on the founders and the CEO.

1 of the mistakes I see is that, this might not answer the question, but 1 of the mistakes I see is that data becomes someone's responsibility who's like a data engineer, but no 1 else's.

And then you're not really succeeding because the goal is to use this data to make decisions throughout the entire organization.

And then you have to make the data accessible and available.

And if you start building all technology first, it's not going to be 100% accessible or available because you have to build dashboards, you have to build all kinds of visualization things.

So the number 1 goal is not to build everything.

The number 1 goal is to make it available, accessible for everyone so everyone can start making decisions.

And Mixpanel and Antitude is great to start with.

You can give everyone a login and everyone has access.

You can build email reports, all kinds of things you can build in day 1.

And just building an email report from your own system, that takes a lot of work.

So there are a lot of benefits to start with something external, and I would recommend most people that.

Yeah, and I think that's probably the single biggest statement we heard as well when we were interviewing all the growth experts, was you need a common source of truth, which is why you use these tools, or you try to build something internal if you feel the tools are not helping you build a common source of truth.

But the more important thing is engagement from the CEO and alignment on taking action based on data.

This is not, you know, and this is a very difficult thing for a CEO to do as well, especially if you're a product driven CEO, right?

Because there's always intuition and there's always data.

And it's not necessary that data always drives all decisions.

Sometimes you want to run experiments because, or you want to do build things which are not driven by data.

And you know, but I think that the CEO that sort of knows or learns how to balance both, it actually gets the most out of it.

And to this day, I think from all the interviews, I'd say the CEO that really stood out in that decision making is actually Zuckerberg at Facebook.

Pretty much every growth expert that I had spoken to who either worked at Facebook, still works at Facebook, or now no longer, but used to, said that Zuck was very clear when the data came to pose a question.

But at the same time, if he wanted a product decision to be pursued, which the data didn't support, he was clear about that as well, saying, look, my gut says this is the right way to try, let's try it.

And in that, if you use scenario B, where you're not using data, but you're using some product intuition that you have, the growth team actually works as defense.

So say you make a change, and the change probably is slightly more detrimental than you thought.

The growth team can actually discover the impact in minutes, not hours, not days.

And so a great growth team can alert pretty quickly and course correct if the need be.

So if you remember the famous slogan that Facebook had, which is, I think it's like, you know, do, think, I forgot the exact.

They dropped the break things now because they have 1000000000 users.

And this is exactly why you can't afford to break things when you have 1000000000 users.

Well, they break things for some fraction of those users.

So I think that you as a, The single most important thing is if you decide to form a growth team, the CEO has to be aligned.

It's not the growth team's responsibility.

They won't be set up for success if the CEO is not aligned.

And so it's extremely important that the CEO endorses the goal of the entire growth team with the company, and when data is surfaced, is asking objectively the right questions, and helping different teams make decisions based on the data.

Paul Matzkoff Were there moments at Airbnb where you guys were like, we're just going to go with our gut on this 1?

And this is a great kind of way to talk about experimentation.

So when you start with a product, most of the ideas you have, like, I know what I'm building.

And you build it, and you talk to users, you build more things.

At some point, your product is so complex, so many things in the product that you make a change, a human cannot fully comprehend all the impacts that that change will have.

So software and metrics will, because software keeps track of everything.

So Airbnb and Facebook are certainly at the stage where if you make a significant change or a small change on the product, then it will have some impact down the line that we cannot comprehend or we do not kind of foresee that that happens.

So In order to solve that, you run experiments.

So experiments, basically, I divide the user group of Airbnb into 2 groups, a control and experiment.

I launch the change in my experiment group, and I look at all the metrics that I care about and see how they change.

So Let's say I changed the date picker on the Airbnb website, because I have a new idea how to make it better.

Well, I really want to see if the search with dates is going up or down.

And at that point, you don't really know.

Like, we run this thing called Experiment Review at Airbnb, and I think Facebook do as well, where you literally are, take the entire team in a room, you show a bunch of experiments that you've been working on, before you tell the results, you ask everyone what to think.

It turns out that the room is very often divided and they have different opinions, but that's because making product decisions are really hard.

It's really, really hard when you have such a scale and such a complex product to know what's going to perform better or worse for certain metrics.

Now, growth teams tend to attract people who are very black and white in their mindset because they can use data to hit other people in the head and be like, no, look at the data.

That's not a good approach because it just is much more complicated than that.

Like that, the vision of the company might not end up exactly the place of the experiment.

I take booking.com as an example of a product that is probably very successful, but not a product that I'm super proud of to be working for.

Because if you experience it, it is incredibly in your face.

There's pop-up and there's things that go all over.

And it's great for conversion, But I wonder if it's something that like a product designer will proud of.

Now you can still do all those things right and still break, build a really awesome product.

I think Facebook and Instagram are example of something that is very, very optimized, very, very good, but it doesn't feel like you're, like it doesn't feel like you're being sold things all the time.

So I think you can get to a point where you combine a strong product vision, you have some kind of design guidelines or product guidelines and still use experimentation and validation to make your decision.

Is there a good example you guys know of that where like, I mean probably Airbnb where you push something forward and say like, this is optimizing the funnel for us.

This is better, but I don't know something about it is just like a little too hacky or whatever term you might have used where you had to pull back

now I think I think that absolutely happens but I think you have to What people that work in growth tend to do bad is explain the purpose of what they're doing.

So a lot of people that work in growth, let's say you work in SEO.

There are a lot of people on Google, like all of us, that go to Google to look for answers.

And if your product does not show up on those first 3 links on Google, you're not gonna be clicked on, and you're not actually there.

SEO is a way to get your product to be 1 of those 3 answers.

So people are not, you're not hacking people's mind, you're not doing anything kind of abusive by doing SEO in any way, you're literally trying to help people that are on Google trying to find answers to questions.

And if Airbnb offers the best product to people that are looking for vacation rentals in the world, we should be on the top 3 results on Google.

And I think that's a great example where like you just have to change the way you talk about it and people will appreciate it a lot more.

Why it's so important to make all this effort into being on top of Google.

Another example I can give is actually Facebook versus Instagram.

You know, Facebook was actually known for its probably on the higher end of aggressive growth tactics in the early days, because when they were building social network, there was no other social network that had more than 50 million MAUs.

So people thought that they would cap at 100 million, right?

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Growth Office Hours with Anu Hariharan and Gustaf Alstromer